{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第二步：调整树的参数：max_depth & min_child_weight（细调）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在上一步粗调最佳参数周围，将步长降为1，进行精细调整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[19:43:48] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 58 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[19:43:48] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[19:43:49] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 62 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[19:43:50] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 30 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[19:43:50] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 56 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[19:43:51] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 52 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[19:43:52] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 54 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[19:43:52] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\n",
      "[19:43:53] C:\\dev\\libs\\xgboost\\src\\tree\\updater_prune.cc:74: tree pruning end, 1 roots, 60 extra nodes, 0 pruned nodes, max_depth=5\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "#from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "from matplotlib import pyplot\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "if __name__ == '__main__':                  ##  这里如果不加这条主循环判断包装语句，代码会在网格搜索的fit语句处报一个关于并行计算的BUG\n",
    "                                            ##  最后经百度找到了这个解决方案，所以为了方便我把所有代码写在了同一个代码框里\n",
    "    #读取训练数据\n",
    "    train = pd.read_csv(\"RentListingInquries_FE_train.csv\")\n",
    "    # 取出特征与标签\n",
    "    y_train = train[\"interest_level\"].values\n",
    "    train = train.drop([\"interest_level\"], axis=1)\n",
    "    X_train = np.array(train)\n",
    "    \n",
    "    #上一步粗调得到max_depth为5 ，细调范围改为[4,5,6]；min_child_weight上一步得到1，细调范围改为[1,2]\n",
    "    max_depth =[4,5,6]\n",
    "    min_child_weight = [1,2]\n",
    "    param_test2_2 = dict(max_depth=max_depth, min_child_weight=min_child_weight)\n",
    "    \n",
    "    # prepare cross validation\n",
    "    kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=3)\n",
    "    \n",
    "    xgb2_2 = XGBClassifier( \n",
    "         learning_rate =0.1,\n",
    "         n_estimators=608,  #第一轮参数调整得到的n_estimators最优值\n",
    "         max_depth=5,\n",
    "         min_child_weight=1,\n",
    "         gamma=0,\n",
    "         subsample=0.3,\n",
    "         colsample_bytree=0.8,\n",
    "         colsample_bylevel = 0.7,\n",
    "         objective= 'multi:softprob',\n",
    "         seed=3,\n",
    "         silent=0)\n",
    "    gsearch2_2 = GridSearchCV(xgb2_2, param_grid = param_test2_2, scoring='neg_log_loss',n_jobs=-1, cv=kfold)\n",
    "    gsearch2_2.fit(X_train , y_train)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([mean: -0.59139, std: 0.00319, params: {'max_depth': 4, 'min_child_weight': 1},\n",
       "  mean: -0.59138, std: 0.00365, params: {'max_depth': 4, 'min_child_weight': 2},\n",
       "  mean: -0.58813, std: 0.00425, params: {'max_depth': 5, 'min_child_weight': 1},\n",
       "  mean: -0.58866, std: 0.00288, params: {'max_depth': 5, 'min_child_weight': 2},\n",
       "  mean: -0.58947, std: 0.00314, params: {'max_depth': 6, 'min_child_weight': 1},\n",
       "  mean: -0.58959, std: 0.00404, params: {'max_depth': 6, 'min_child_weight': 2}],\n",
       " {'max_depth': 5, 'min_child_weight': 1},\n",
       " -0.5881333281807066)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsearch2_2.grid_scores_, gsearch2_2.best_params_,     gsearch2_2.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "两个参数调整结果依然是5和1为最佳"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: -0.588133 using {'max_depth': 5, 'min_child_weight': 1}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (gsearch2_2.best_score_, gsearch2_2.best_params_))\n",
    "test_means = gsearch2_2.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = gsearch2_2.cv_results_[ 'std_test_score' ]\n",
    "train_means = gsearch2_2.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = gsearch2_2.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "pd.DataFrame(gsearch2_2.cv_results_).to_csv('my_preds_maxdepth_min_child_weights_2.csv')\n",
    "\n",
    "# plot results\n",
    "test_scores = np.array(test_means).reshape(len(min_child_weight), len(max_depth))\n",
    "train_scores = np.array(train_means).reshape(len(min_child_weight), len(max_depth))\n",
    "\n",
    "for i, value in enumerate(min_child_weight):\n",
    "    pyplot.plot(max_depth, test_scores[i], label= 'test_min_child_weight:'   + str(value))\n",
    "#for i, value in enumerate(min_child_weight):\n",
    "#    pyplot.plot(max_depth, train_scores[i], label= 'train_min_child_weight:'   + str(value))\n",
    "    \n",
    "pyplot.legend()\n",
    "pyplot.xlabel( 'max_depth' )                                                                                                      \n",
    "pyplot.ylabel( '- Log Loss' )\n",
    "pyplot.savefig( 'max_depth_vs_min_child_weght2.png' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
